有人可以帮我吗

Question

import tensorflow as tf
import numpy as np



def loadMNIST( prefix, folder ):
    intType = np.dtype( 'int32' ).newbyteorder( '>' )
    nMetaDataBytes = 4 * intType.itemsize

    data = np.fromfile( folder + "\\" + prefix + '-images.idx3-ubyte', dtype = 'ubyte' )
    magicBytes, nImages, width, height = np.frombuffer( data[:nMetaDataBytes].tobytes(), intType )
    data = data[nMetaDataBytes:].astype( dtype = 'float32' ).reshape( [ nImages, width, height ] )

    labels = np.fromfile( folder + "/" + prefix + '-labels.idx1-ubyte',
                          dtype = 'ubyte' )[2 * intType.itemsize:]

    return data, labels




def train_neural_network(x):
    prediction = convolutional_neural_network(x)
    cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits_v2(labels=ph_y, logits=prediction))
    optimizer = tf.train.AdamOptimizer().minimize(cost)

    with tf.Session() as sess:
        sess.run(tf.initialize_all_variables())
        for epoch in range(hm_epochs):
            epoch_loss = 0
            i = 0
            while i < len(trainingImages):
                start = i
                end = i + btch_size
                batch_x = np.array(trainingImages[start:end])
                batch_y = np.array(trainingLabels[start:end])

                _, c = sess.run([optimizer, cost], feed_dict={x: batch_x,ph_y: batch_y})
                epoch_loss += c
                i += btch_size

            print('Epoch', epoch + 1, 'completed out of', hm_epochs, 'loss:', epoch_loss)
        correct = tf.equal(tf.argmax(prediction, 1), tf.argmax(ph_y, 1))
        accuracy = tf.reduce_mean(tf.cast(correct, 'float'))

        print('Accuracy:', accuracy.eval({x: testImages, ph_y: testLabels}))